Overview

Dataset statistics

Number of variables12
Number of observations823
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory77.3 KiB
Average record size in memory96.2 B

Variable types

Categorical5
Numeric7

Alerts

Model(Year) has constant value "2023"Constant
Model.1 has a high cardinality: 647 distinct valuesHigh cardinality
Engine Size(L) is highly overall correlated with Cylinders and 6 other fieldsHigh correlation
Cylinders is highly overall correlated with Engine Size(L) and 6 other fieldsHigh correlation
Fuel(Type) is highly overall correlated with Engine Size(L) and 6 other fieldsHigh correlation
Fuel Consumption(City (L/100 km) is highly overall correlated with Engine Size(L) and 5 other fieldsHigh correlation
CO2 Emissions(g/km) is highly overall correlated with Engine Size(L) and 5 other fieldsHigh correlation
CO2(Rating) is highly overall correlated with Engine Size(L) and 5 other fieldsHigh correlation
Smog(Rating) is highly overall correlated with Engine Size(L) and 5 other fieldsHigh correlation
Make is highly overall correlated with Engine Size(L) and 2 other fieldsHigh correlation
Model.1 is uniformly distributedUniform

Reproduction

Analysis started2023-09-08 11:01:02.235505
Analysis finished2023-09-08 11:01:27.068167
Duration24.83 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Model(Year)
Categorical

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
2023
823 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3292
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2023 823
100.0%

Length

2023-09-08T11:01:27.506652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-08T11:01:28.154269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 823
100.0%

Most occurring characters

ValueCountFrequency (%)
2 1646
50.0%
0 823
25.0%
3 823
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3292
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1646
50.0%
0 823
25.0%
3 823
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3292
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1646
50.0%
0 823
25.0%
3 823
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3292
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1646
50.0%
0 823
25.0%
3 823
25.0%

Make
Categorical

Distinct39
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
Ford
93 
Chevrolet
55 
BMW
 
51
Toyota
 
46
Audi
 
44
Other values (34)
534 

Length

Max length13
Median length11
Mean length6.1178615
Min length3

Characters and Unicode

Total characters5035
Distinct characters45
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAcura
2nd rowAcura
3rd rowAcura
4th rowAcura
5th rowAcura

Common Values

ValueCountFrequency (%)
Ford 93
 
11.3%
Chevrolet 55
 
6.7%
BMW 51
 
6.2%
Toyota 46
 
5.6%
Audi 44
 
5.3%
Mercedes-Benz 44
 
5.3%
GMC 36
 
4.4%
Jeep 30
 
3.6%
Nissan 27
 
3.3%
Lexus 26
 
3.2%
Other values (29) 371
45.1%

Length

2023-09-08T11:01:29.149319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ford 93
 
10.8%
chevrolet 55
 
6.4%
bmw 51
 
5.9%
toyota 46
 
5.4%
audi 44
 
5.1%
mercedes-benz 44
 
5.1%
gmc 36
 
4.2%
jeep 30
 
3.5%
nissan 27
 
3.1%
cadillac 26
 
3.0%
Other values (32) 406
47.3%

Most occurring characters

ValueCountFrequency (%)
e 515
 
10.2%
o 404
 
8.0%
a 394
 
7.8%
d 317
 
6.3%
r 302
 
6.0%
i 244
 
4.8%
n 222
 
4.4%
s 219
 
4.3%
M 208
 
4.1%
l 183
 
3.6%
Other values (35) 2027
40.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3788
75.2%
Uppercase Letter 1161
 
23.1%
Dash Punctuation 51
 
1.0%
Space Separator 35
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 515
13.6%
o 404
10.7%
a 394
10.4%
d 317
 
8.4%
r 302
 
8.0%
i 244
 
6.4%
n 222
 
5.9%
s 219
 
5.8%
l 183
 
4.8%
u 165
 
4.4%
Other values (14) 823
21.7%
Uppercase Letter
ValueCountFrequency (%)
M 208
17.9%
C 124
10.7%
B 113
9.7%
F 94
 
8.1%
A 69
 
5.9%
L 61
 
5.3%
I 57
 
4.9%
R 53
 
4.6%
N 52
 
4.5%
W 51
 
4.4%
Other values (9) 279
24.0%
Dash Punctuation
ValueCountFrequency (%)
- 51
100.0%
Space Separator
ValueCountFrequency (%)
35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4949
98.3%
Common 86
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 515
 
10.4%
o 404
 
8.2%
a 394
 
8.0%
d 317
 
6.4%
r 302
 
6.1%
i 244
 
4.9%
n 222
 
4.5%
s 219
 
4.4%
M 208
 
4.2%
l 183
 
3.7%
Other values (33) 1941
39.2%
Common
ValueCountFrequency (%)
- 51
59.3%
35
40.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5035
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 515
 
10.2%
o 404
 
8.0%
a 394
 
7.8%
d 317
 
6.3%
r 302
 
6.0%
i 244
 
4.8%
n 222
 
4.4%
s 219
 
4.3%
M 208
 
4.1%
l 183
 
3.6%
Other values (35) 2027
40.3%

Model.1
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct647
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
Mustang
 
5
F-150 4X4 FFV
 
4
Silverado 4WD Mud Terrain Tire
 
4
Civic Hatchback
 
4
Sierra 4WD Mud Terrain Tire
 
4
Other values (642)
802 

Length

Max length46
Median length35
Mean length14.844471
Min length1

Characters and Unicode

Total characters12217
Distinct characters70
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique514 ?
Unique (%)62.5%

Sample

1st rowIntegra
2nd rowIntegra A-SPEC
3rd rowIntegra A-SPEC
4th rowMDX SH-AWD
5th rowMDX SH-AWD Type S

Common Values

ValueCountFrequency (%)
Mustang 5
 
0.6%
F-150 4X4 FFV 4
 
0.5%
Silverado 4WD Mud Terrain Tire 4
 
0.5%
Civic Hatchback 4
 
0.5%
Sierra 4WD Mud Terrain Tire 4
 
0.5%
F-150 FFV 4
 
0.5%
F-150 FFV (Without Stop-Start) 4
 
0.5%
Mustang Convertible 4
 
0.5%
Silverado 4WD 4
 
0.5%
Camaro 4
 
0.5%
Other values (637) 782
95.0%

Length

2023-09-08T11:01:29.982400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
awd 173
 
7.8%
4wd 98
 
4.4%
4x4 65
 
2.9%
quattro 41
 
1.9%
4matic 40
 
1.8%
coupe 34
 
1.5%
f-150 34
 
1.5%
ffv 30
 
1.4%
cooper 25
 
1.1%
sedan 25
 
1.1%
Other values (450) 1647
74.5%

Most occurring characters

ValueCountFrequency (%)
1389
 
11.4%
r 745
 
6.1%
o 683
 
5.6%
e 663
 
5.4%
a 652
 
5.3%
t 475
 
3.9%
i 428
 
3.5%
n 391
 
3.2%
4 378
 
3.1%
D 369
 
3.0%
Other values (60) 6044
49.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6078
49.8%
Uppercase Letter 3357
27.5%
Space Separator 1389
 
11.4%
Decimal Number 1143
 
9.4%
Dash Punctuation 125
 
1.0%
Close Punctuation 49
 
0.4%
Open Punctuation 49
 
0.4%
Other Punctuation 17
 
0.1%
Math Symbol 10
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 745
12.3%
o 683
11.2%
e 663
10.9%
a 652
10.7%
t 475
 
7.8%
i 428
 
7.0%
n 391
 
6.4%
l 272
 
4.5%
u 263
 
4.3%
d 213
 
3.5%
Other values (17) 1293
21.3%
Uppercase Letter
ValueCountFrequency (%)
D 369
11.0%
C 340
 
10.1%
W 339
 
10.1%
S 326
 
9.7%
A 300
 
8.9%
T 234
 
7.0%
M 177
 
5.3%
X 152
 
4.5%
F 144
 
4.3%
R 125
 
3.7%
Other values (16) 851
25.4%
Decimal Number
ValueCountFrequency (%)
4 378
33.1%
0 250
21.9%
5 209
18.3%
3 97
 
8.5%
1 76
 
6.6%
6 38
 
3.3%
8 34
 
3.0%
2 27
 
2.4%
7 18
 
1.6%
9 16
 
1.4%
Other Punctuation
ValueCountFrequency (%)
/ 12
70.6%
. 5
29.4%
Space Separator
ValueCountFrequency (%)
1389
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 125
100.0%
Close Punctuation
ValueCountFrequency (%)
) 49
100.0%
Open Punctuation
ValueCountFrequency (%)
( 49
100.0%
Math Symbol
ValueCountFrequency (%)
+ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9435
77.2%
Common 2782
 
22.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 745
 
7.9%
o 683
 
7.2%
e 663
 
7.0%
a 652
 
6.9%
t 475
 
5.0%
i 428
 
4.5%
n 391
 
4.1%
D 369
 
3.9%
C 340
 
3.6%
W 339
 
3.6%
Other values (43) 4350
46.1%
Common
ValueCountFrequency (%)
1389
49.9%
4 378
 
13.6%
0 250
 
9.0%
5 209
 
7.5%
- 125
 
4.5%
3 97
 
3.5%
1 76
 
2.7%
) 49
 
1.8%
( 49
 
1.8%
6 38
 
1.4%
Other values (7) 122
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12205
99.9%
None 12
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1389
 
11.4%
r 745
 
6.1%
o 683
 
5.6%
e 663
 
5.4%
a 652
 
5.3%
t 475
 
3.9%
i 428
 
3.5%
n 391
 
3.2%
4 378
 
3.1%
D 369
 
3.0%
Other values (58) 6032
49.4%
None
ValueCountFrequency (%)
é 8
66.7%
á 4
33.3%

Vehicle Class
Categorical

Distinct14
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
SUV: Small
194 
SUV: Standard
133 
Mid-size
111 
Pickup truck: Standard
97 
Subcompact
75 
Other values (9)
213 

Length

Max length23
Median length22
Mean length11.918591
Min length7

Characters and Unicode

Total characters9809
Distinct characters32
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFull-size
2nd rowFull-size
3rd rowFull-size
4th rowSUV: Small
5th rowSUV: Standard

Common Values

ValueCountFrequency (%)
SUV: Small 194
23.6%
SUV: Standard 133
16.2%
Mid-size 111
13.5%
Pickup truck: Standard 97
11.8%
Subcompact 75
 
9.1%
Compact 59
 
7.2%
Full-size 47
 
5.7%
Two-seater 35
 
4.3%
Minicompact 21
 
2.6%
Pickup truck: Small 17
 
2.1%
Other values (4) 34
 
4.1%

Length

2023-09-08T11:01:30.802185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
suv 327
22.8%
standard 230
16.1%
small 225
15.7%
mid-size 119
 
8.3%
pickup 114
 
8.0%
truck 114
 
8.0%
subcompact 75
 
5.2%
compact 59
 
4.1%
full-size 47
 
3.3%
two-seater 35
 
2.4%
Other values (7) 87
 
6.1%

Most occurring characters

ValueCountFrequency (%)
a 931
 
9.5%
S 884
 
9.0%
609
 
6.2%
d 579
 
5.9%
t 578
 
5.9%
l 554
 
5.6%
c 489
 
5.0%
i 487
 
5.0%
: 463
 
4.7%
r 384
 
3.9%
Other values (22) 3851
39.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6596
67.2%
Uppercase Letter 1940
 
19.8%
Space Separator 609
 
6.2%
Other Punctuation 463
 
4.7%
Dash Punctuation 201
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 931
14.1%
d 579
 
8.8%
t 578
 
8.8%
l 554
 
8.4%
c 489
 
7.4%
i 487
 
7.4%
r 384
 
5.8%
m 380
 
5.8%
u 355
 
5.4%
n 309
 
4.7%
Other values (11) 1550
23.5%
Uppercase Letter
ValueCountFrequency (%)
S 884
45.6%
V 327
 
16.9%
U 327
 
16.9%
M 147
 
7.6%
P 114
 
5.9%
C 59
 
3.0%
F 47
 
2.4%
T 35
 
1.8%
Space Separator
ValueCountFrequency (%)
609
100.0%
Other Punctuation
ValueCountFrequency (%)
: 463
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8536
87.0%
Common 1273
 
13.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 931
 
10.9%
S 884
 
10.4%
d 579
 
6.8%
t 578
 
6.8%
l 554
 
6.5%
c 489
 
5.7%
i 487
 
5.7%
r 384
 
4.5%
m 380
 
4.5%
u 355
 
4.2%
Other values (19) 2915
34.1%
Common
ValueCountFrequency (%)
609
47.8%
: 463
36.4%
- 201
 
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9809
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 931
 
9.5%
S 884
 
9.0%
609
 
6.2%
d 579
 
5.9%
t 578
 
5.9%
l 554
 
5.6%
c 489
 
5.0%
i 487
 
5.0%
: 463
 
4.7%
r 384
 
3.9%
Other values (22) 3851
39.3%

Engine Size(L)
Real number (ℝ)

Distinct31
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1399757
Minimum1.2
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-09-08T11:01:31.673390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile1.5
Q12
median3
Q33.6
95-th percentile6.2
Maximum8
Range6.8
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.3541568
Coefficient of variation (CV)0.43126346
Kurtosis0.37246767
Mean3.1399757
Median Absolute Deviation (MAD)1
Skewness1.0416828
Sum2584.2
Variance1.8337406
MonotonicityNot monotonic
2023-09-08T11:01:32.602211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2 191
23.2%
3 124
15.1%
2.5 63
 
7.7%
3.5 46
 
5.6%
1.5 36
 
4.4%
3.6 36
 
4.4%
5 30
 
3.6%
2.7 30
 
3.6%
4 27
 
3.3%
5.3 26
 
3.2%
Other values (21) 214
26.0%
ValueCountFrequency (%)
1.2 4
 
0.5%
1.3 7
 
0.9%
1.5 36
 
4.4%
1.6 21
 
2.6%
1.8 3
 
0.4%
2 191
23.2%
2.3 25
 
3.0%
2.4 18
 
2.2%
2.5 63
 
7.7%
2.7 30
 
3.6%
ValueCountFrequency (%)
8 3
 
0.4%
6.7 7
 
0.9%
6.4 10
 
1.2%
6.2 26
3.2%
6 4
 
0.5%
5.7 13
1.6%
5.6 2
 
0.2%
5.5 2
 
0.2%
5.3 26
3.2%
5.2 13
1.6%

Cylinders
Real number (ℝ)

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6038882
Minimum3
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-09-08T11:01:33.029921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q14
median6
Q36
95-th percentile8
Maximum16
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9541171
Coefficient of variation (CV)0.34870737
Kurtosis2.930275
Mean5.6038882
Median Absolute Deviation (MAD)2
Skewness1.3265945
Sum4612
Variance3.8185737
MonotonicityNot monotonic
2023-09-08T11:01:33.456990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 364
44.2%
6 247
30.0%
8 162
19.7%
3 24
 
2.9%
12 14
 
1.7%
10 9
 
1.1%
16 3
 
0.4%
ValueCountFrequency (%)
3 24
 
2.9%
4 364
44.2%
6 247
30.0%
8 162
19.7%
10 9
 
1.1%
12 14
 
1.7%
16 3
 
0.4%
ValueCountFrequency (%)
16 3
 
0.4%
12 14
 
1.7%
10 9
 
1.1%
8 162
19.7%
6 247
30.0%
4 364
44.2%
3 24
 
2.9%

Transmission
Categorical

Distinct23
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
AS8
199 
A8
105 
AS10
102 
A10
59 
A9
59 
Other values (18)
299 

Length

Max length4
Median length3
Mean length2.800729
Min length2

Characters and Unicode

Total characters2305
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAV7
2nd rowAV7
3rd rowM6
4th rowAS10
5th rowAS10

Common Values

ValueCountFrequency (%)
AS8 199
24.2%
A8 105
12.8%
AS10 102
12.4%
A10 59
 
7.2%
A9 59
 
7.2%
M6 57
 
6.9%
AM7 49
 
6.0%
AV 40
 
4.9%
AS6 27
 
3.3%
AM8 24
 
2.9%
Other values (13) 102
12.4%

Length

2023-09-08T11:01:33.941455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
as8 199
24.2%
a8 105
12.8%
as10 102
12.4%
a10 59
 
7.2%
a9 59
 
7.2%
m6 57
 
6.9%
am7 49
 
6.0%
av 40
 
4.9%
as6 27
 
3.3%
am8 24
 
2.9%
Other values (13) 102
12.4%

Most occurring characters

ValueCountFrequency (%)
A 757
32.8%
S 356
15.4%
8 351
15.2%
1 172
 
7.5%
0 168
 
7.3%
M 148
 
6.4%
6 105
 
4.6%
V 93
 
4.0%
9 81
 
3.5%
7 69
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1354
58.7%
Decimal Number 951
41.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 351
36.9%
1 172
18.1%
0 168
17.7%
6 105
 
11.0%
9 81
 
8.5%
7 69
 
7.3%
5 5
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
A 757
55.9%
S 356
26.3%
M 148
 
10.9%
V 93
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 1354
58.7%
Common 951
41.3%

Most frequent character per script

Common
ValueCountFrequency (%)
8 351
36.9%
1 172
18.1%
0 168
17.7%
6 105
 
11.0%
9 81
 
8.5%
7 69
 
7.3%
5 5
 
0.5%
Latin
ValueCountFrequency (%)
A 757
55.9%
S 356
26.3%
M 148
 
10.9%
V 93
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2305
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 757
32.8%
S 356
15.4%
8 351
15.2%
1 172
 
7.5%
0 168
 
7.3%
M 148
 
6.4%
6 105
 
4.6%
V 93
 
4.0%
9 81
 
3.5%
7 69
 
3.0%

Fuel(Type)
Real number (ℝ)

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6038882
Minimum3
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-09-08T11:01:34.404562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q14
median6
Q36
95-th percentile8
Maximum16
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9541171
Coefficient of variation (CV)0.34870737
Kurtosis2.930275
Mean5.6038882
Median Absolute Deviation (MAD)2
Skewness1.3265945
Sum4612
Variance3.8185737
MonotonicityNot monotonic
2023-09-08T11:01:34.852304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 364
44.2%
6 247
30.0%
8 162
19.7%
3 24
 
2.9%
12 14
 
1.7%
10 9
 
1.1%
16 3
 
0.4%
ValueCountFrequency (%)
3 24
 
2.9%
4 364
44.2%
6 247
30.0%
8 162
19.7%
10 9
 
1.1%
12 14
 
1.7%
16 3
 
0.4%
ValueCountFrequency (%)
16 3
 
0.4%
12 14
 
1.7%
10 9
 
1.1%
8 162
19.7%
6 247
30.0%
4 364
44.2%
3 24
 
2.9%
Distinct151
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.37983
Minimum4.4
Maximum30.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-09-08T11:01:35.513259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.4
5-th percentile7.61
Q110.1
median12.1
Q314.6
95-th percentile18.1
Maximum30.3
Range25.9
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.4207406
Coefficient of variation (CV)0.27631564
Kurtosis1.761591
Mean12.37983
Median Absolute Deviation (MAD)2.1
Skewness0.68955748
Sum10188.6
Variance11.701466
MonotonicityNot monotonic
2023-09-08T11:01:36.141082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.1 20
 
2.4%
11 19
 
2.3%
13.1 17
 
2.1%
15.2 16
 
1.9%
11.2 15
 
1.8%
12.3 13
 
1.6%
9 13
 
1.6%
10.9 13
 
1.6%
14.1 13
 
1.6%
11.1 13
 
1.6%
Other values (141) 671
81.5%
ValueCountFrequency (%)
4.4 1
 
0.1%
4.5 2
0.2%
4.6 1
 
0.1%
4.8 2
0.2%
4.9 1
 
0.1%
5 2
0.2%
5.2 1
 
0.1%
5.3 2
0.2%
5.5 1
 
0.1%
5.6 3
0.4%
ValueCountFrequency (%)
30.3 2
0.2%
26.8 1
 
0.1%
22.8 1
 
0.1%
22.4 1
 
0.1%
21.6 1
 
0.1%
21.3 4
0.5%
20.7 1
 
0.1%
20.5 1
 
0.1%
20.4 1
 
0.1%
20.3 2
0.2%

CO2 Emissions(g/km)
Real number (ℝ)

Distinct232
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean256.45322
Minimum104
Maximum608
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-09-08T11:01:36.688557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum104
5-th percentile162.3
Q1211
median254
Q3298
95-th percentile359
Maximum608
Range504
Interquartile range (IQR)87

Descriptive statistics

Standard deviation63.412909
Coefficient of variation (CV)0.24726891
Kurtosis1.952496
Mean256.45322
Median Absolute Deviation (MAD)44
Skewness0.60493243
Sum211061
Variance4021.197
MonotonicityNot monotonic
2023-09-08T11:01:37.531714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
281 13
 
1.6%
277 12
 
1.5%
271 11
 
1.3%
265 10
 
1.2%
275 9
 
1.1%
299 9
 
1.1%
242 9
 
1.1%
221 9
 
1.1%
284 9
 
1.1%
253 9
 
1.1%
Other values (222) 723
87.8%
ValueCountFrequency (%)
104 1
 
0.1%
110 2
0.2%
111 1
 
0.1%
112 1
 
0.1%
113 1
 
0.1%
115 1
 
0.1%
117 1
 
0.1%
121 1
 
0.1%
124 3
0.4%
130 1
 
0.1%
ValueCountFrequency (%)
608 2
0.2%
522 1
0.1%
465 1
0.1%
460 1
0.1%
436 1
0.1%
410 1
0.1%
397 2
0.2%
395 1
0.1%
392 1
0.1%
389 2
0.2%

CO2(Rating)
Real number (ℝ)

Distinct9
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5407047
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-09-08T11:01:38.008354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q35
95-th percentile7
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2664424
Coefficient of variation (CV)0.27890878
Kurtosis0.4073767
Mean4.5407047
Median Absolute Deviation (MAD)1
Skewness0.057832375
Sum3737
Variance1.6038764
MonotonicityNot monotonic
2023-09-08T11:01:38.431131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 264
32.1%
4 245
29.8%
6 120
14.6%
3 106
12.9%
2 38
 
4.6%
7 29
 
3.5%
8 13
 
1.6%
1 7
 
0.9%
9 1
 
0.1%
ValueCountFrequency (%)
1 7
 
0.9%
2 38
 
4.6%
3 106
12.9%
4 245
29.8%
5 264
32.1%
6 120
14.6%
7 29
 
3.5%
8 13
 
1.6%
9 1
 
0.1%
ValueCountFrequency (%)
9 1
 
0.1%
8 13
 
1.6%
7 29
 
3.5%
6 120
14.6%
5 264
32.1%
4 245
29.8%
3 106
12.9%
2 38
 
4.6%
1 7
 
0.9%

Smog(Rating)
Real number (ℝ)

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2430134
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-09-08T11:01:38.952596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median5
Q37
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6688783
Coefficient of variation (CV)0.31830518
Kurtosis0.15430395
Mean5.2430134
Median Absolute Deviation (MAD)2
Skewness-0.87024141
Sum4315
Variance2.7851549
MonotonicityNot monotonic
2023-09-08T11:01:39.500723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 277
33.7%
7 239
29.0%
6 133
16.2%
3 125
15.2%
1 44
 
5.3%
8 5
 
0.6%
ValueCountFrequency (%)
1 44
 
5.3%
3 125
15.2%
5 277
33.7%
6 133
16.2%
7 239
29.0%
8 5
 
0.6%
ValueCountFrequency (%)
8 5
 
0.6%
7 239
29.0%
6 133
16.2%
5 277
33.7%
3 125
15.2%
1 44
 
5.3%

Interactions

2023-09-08T11:01:21.587063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:03.994042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:07.189348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:10.816332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:13.854259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:16.436113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:18.891579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:21.915480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:04.567092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:07.634279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:11.412811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:14.188253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:16.796141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:19.258289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:22.258349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:05.123434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:08.073649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:12.023393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:14.535175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:17.126832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:19.605193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:22.738325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:05.521657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:08.547559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:12.354647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:14.858773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:17.498904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:19.934447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:23.219987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:05.886898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:09.055791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:12.726535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:15.192155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:17.824342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:20.258477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:23.682140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:06.358965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:09.513841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:13.098674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:15.535197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:18.193756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:20.654805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:24.323911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:06.807362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:10.106098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:13.490937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:16.018169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:18.569746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T11:01:21.173259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-08T11:01:39.809896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Engine Size(L)CylindersFuel(Type)Fuel Consumption(City (L/100 km)CO2 Emissions(g/km)CO2(Rating)Smog(Rating)MakeVehicle ClassTransmission
Engine Size(L)1.0000.9410.9410.8420.822-0.797-0.5240.6090.3060.325
Cylinders0.9411.0001.0000.8420.820-0.800-0.5820.6820.3760.314
Fuel(Type)0.9411.0001.0000.8420.820-0.800-0.5820.6820.3760.314
Fuel Consumption(City (L/100 km)0.8420.8420.8421.0000.971-0.936-0.5700.4340.2220.328
CO2 Emissions(g/km)0.8220.8200.8200.9711.000-0.967-0.5690.4400.2210.312
CO2(Rating)-0.797-0.800-0.800-0.936-0.9671.0000.5500.3950.2550.363
Smog(Rating)-0.524-0.582-0.582-0.570-0.5690.5501.0000.4130.2330.342
Make0.6090.6820.6820.4340.4400.3950.4131.0000.3760.470
Vehicle Class0.3060.3760.3760.2220.2210.2550.2330.3761.0000.263
Transmission0.3250.3140.3140.3280.3120.3630.3420.4700.2631.000

Missing values

2023-09-08T11:01:25.199250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-08T11:01:26.565358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Model(Year)MakeModel.1Vehicle ClassEngine Size(L)CylindersTransmissionFuel(Type)Fuel Consumption(City (L/100 km)CO2 Emissions(g/km)CO2(Rating)Smog(Rating)
02023AcuraIntegraFull-size1.54.0AV74.07.916767
12023AcuraIntegra A-SPECFull-size1.54.0AV74.08.117267
22023AcuraIntegra A-SPECFull-size1.54.0M64.08.918166
32023AcuraMDX SH-AWDSUV: Small3.56.0AS106.012.626345
42023AcuraMDX SH-AWD Type SSUV: Standard3.06.0AS106.013.829145
52023AcuraRDX SH-AWDSUV: Small2.04.0AS104.011.023256
62023AcuraRDX SH-AWD A-SPECSUV: Small2.04.0AS104.011.324256
72023AcuraTLX SH-AWDCompact2.04.0AS104.011.223057
82023AcuraTLX SH-AWD A-SPECCompact2.04.0AS104.011.323157
92023AcuraTLX Type SCompact3.06.0AS106.012.325655
Model(Year)MakeModel.1Vehicle ClassEngine Size(L)CylindersTransmissionFuel(Type)Fuel Consumption(City (L/100 km)CO2 Emissions(g/km)CO2(Rating)Smog(Rating)
8132023VolvoS90 B6 AWDMid-size2.04.0AS84.010.321157
8142023VolvoV60 B6 AWDStation wagon: Small2.04.0AS84.010.321257
8152023VolvoV60 CC B5 AWDStation wagon: Small2.04.0AS84.010.121155
8162023VolvoV90 CC B6 AWDStation wagon: Mid-size2.04.0AS84.010.922757
8172023VolvoXC40 B4 AWDSUV: Small2.04.0AS84.010.020855
8182023VolvoXC40 B5 AWDSUV: Small2.04.0AS84.010.221555
8192023VolvoXC60 B5 AWDSUV: Small2.04.0AS84.010.321855
8202023VolvoXC60 B6 AWDSUV: Small2.04.0AS84.011.123357
8212023VolvoXC90 B5 AWDSUV: Standard2.04.0AS84.010.522355
8222023VolvoXC90 B6 AWDSUV: Standard2.04.0AS84.011.924957